Medical image processing often uses segmentation to determine the location of features of interest in a medical image and to generate a patient-specific model of the anatomical structure in the image. This patient-specific model can then be used to make measurements of the anatomical structure (for example, to measure the left ventricular volume over the heart cycle), for diagnosis, treatment planning (for example, in aortic valve implant fitting), or for the prediction of disease (for example, biophysical models). In order to extract the shape of an anatomical structure from medical images, different methods of segmentation can be used, such as, for example, a model-based segmentation framework in which an adjustable model of the anatomical structure is placed in the image to segment the anatomical structure.
An example of such a model-based segmentation framework is provided in an article by Ecabert et al., entitled “Segmentation of the heart and great vessels in CT images using a model-based adaptation framework, Medical Image Analysis”, Volume 15, Issue 6, December 2011, Pages 863-876, which describes detailed local correction and manual editing of a multi-color mask overlain on an image of a heart.
However, in these existing techniques, if the segmentation of an anatomical structure in an image is inaccurate (for example, if the segmentation results in a model that does not accurately reflect the shape of the anatomical structure in the image), various correction mechanisms are available whereby a physician can manually adjust the model to correct the segmentation result. For example, a physician may manually adjust the model by dragging points of the model towards the correct features of the anatomical structure in the image. For the purposes of efficiency, existing correction methods typically modify the model in multiple images simultaneously as this reduces the manual work involved and can keep the segmentation consistent across the images. For example, a modification may be propagated through multiple images in time-series of two-dimensional images, or through multiple images in time-series of three-dimensional images and/or in all three-dimensions in the case of three-dimensional images.
As a result, not only parts of the model belonging to a currently displayed image are modified, but also parts of the model belonging to neighboring images in the sequence are modified. This means that, following an adjustment to the model in an image, the physician must review neighboring images to ensure that any changes that were propagated through to the neighboring images are reasonable. In some instances, for example, the physician will need to review images that have already been previously inspected to determine whether any modifications that have propagated to those previously inspected images have resulted in errors that require the model to be re-adjusted in those images. In order to ensure that no errors result from a modification to the model in one image, the physician would need to check the model in all other images.
In the case of adjusting a model in a four-dimensional image comprising a time-sequence of three-dimensional images, the propagation of adjustments to the model across the time-sequence of three-dimensional images can occur in both space (for example, to images at different points in space, such as neighboring images) and time (for example, to images at different points in time). In such examples, it becomes even more complicated and time consuming to review adjustments to the model as the adjustments may have been propagated in both space and time.
Thus, the process for adjusting a model of an anatomical structure becomes inefficient once again and is also complex. There is thus a need for an improved method and apparatus for adjusting a model of an anatomical structure in a sequence of images of the anatomical structure.